Using PSO algorithm for power flow management enhancement in PV-battery grid systems

Abstract

In this article, we have shown the possibility of improving the quality of the energy injected into the electrical network and the flexibility of its exchange between the different components of the proposed hybrid network (photovoltaic generator connected to the network-storage battery-load of the DC motor) to develop a control element based on the combination of fuzzy logic and an algorithm derived from PSO Animal Behavior. The proposed control works on DC/AC and bi-directional DC/DC converters, which form the basis of power management between the parts of the proposed hybrid network. MATLAB/Simulink software is used to demonstrate the effectiveness of the proposed control. The results show that the proposed control contributed to the stability of the photovoltaic energy produced, the improvement of the quality of energy injected into the network, as well as the response speed during the process of charging and discharging the battery, which gave more efficiency to the DC motor connected to the DC bus.

Optimal design of BiCMOS second generation current conveyor using the genetic algorithm

“…A few years ago, some (meta-) heuristics were proposed in the literature to deal with the optimal sizing of analog circuits efficiently, including: tabu search (TS) [19], simulated annealing (SA) [20], genetic algorithms (GA) [21]- [23], particle swarm optimization (PSO) [24], [25] ant colony optimization (ACO) [26]- [29] and artificial bee colony (ABC) [30], [31]. The GA is a popular metaheuristic technique known for its ease of use and ability to handle a wide range of optimization, design and application areas [32].…”